



'''simple docstring'''



from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class 						lowerCamelCase_				(       nn.Module      ):







						def __init__(	self :   Any					,     lowerCAmelCase__ :   int = 16					,     lowerCAmelCase__ :   int = 88					,     lowerCAmelCase__ :   Optional[int] = None					,     lowerCAmelCase__ :   int = 1					,     lowerCAmelCase__ :   float = 0.0					,     lowerCAmelCase__ :   int = 32					,     lowerCAmelCase__ :   Optional[int] = None					,     lowerCAmelCase__ :   bool = False					,     lowerCAmelCase__ :   Optional[int] = None					,     lowerCAmelCase__ :   Optional[int] = None					,     lowerCAmelCase__ :   str = "geglu"					,     lowerCAmelCase__ :   Optional[int] = None					,     ):

													"""simple docstring"""
													super().__init__()
													SCREAMING_SNAKE_CASE       :   List[str]         =      nn.ModuleList(
													    [
													        TransformeraDModel(
													            num_attention_heads=lowerCAmelCase__					,     attention_head_dim=lowerCAmelCase__					,     in_channels=lowerCAmelCase__					,     num_layers=lowerCAmelCase__					,     dropout=lowerCAmelCase__					,     norm_num_groups=lowerCAmelCase__					,     cross_attention_dim=lowerCAmelCase__					,     attention_bias=lowerCAmelCase__					,     sample_size=lowerCAmelCase__					,     num_vector_embeds=lowerCAmelCase__					,     activation_fn=lowerCAmelCase__					,     num_embeds_ada_norm=lowerCAmelCase__					,     )
													        for _ in range(2					)
													    ]					)

													# Variables that can be set by a pipeline:

													# The ratio of transformer1 to transformer2's output states to be combined during inference
													SCREAMING_SNAKE_CASE       :   List[str]         =      0.5

													# The shape of `encoder_hidden_states` is expected to be
													# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
													SCREAMING_SNAKE_CASE       :   List[str]         =      [77, 2_57]

													# Which transformer to use to encode which condition.
													# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
													SCREAMING_SNAKE_CASE       :   List[str]         =      [1, 0]







						def        __lowercase     (	self :   str					,     lowerCAmelCase__ :   List[Any]					,     lowerCAmelCase__ :   List[str]					,     lowerCAmelCase__ :   List[str]=None					,     lowerCAmelCase__ :   Union[str, Any]=None					,     lowerCAmelCase__ :   int=None					,     lowerCAmelCase__ :   bool = True					,     ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Any         =      hidden_states

													SCREAMING_SNAKE_CASE       :   Any         =      []
													SCREAMING_SNAKE_CASE       :   int         =      0
													# attention_mask is not used yet
													for i in range(2					):
																				# for each of the two transformers, pass the corresponding condition tokens
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
																				SCREAMING_SNAKE_CASE       :   Any         =      self.transformer_index_for_condition[i]
																				SCREAMING_SNAKE_CASE       :   Optional[Any]         =      self.transformers[transformer_index](
																				    lowerCAmelCase__					,     encoder_hidden_states=lowerCAmelCase__					,     timestep=lowerCAmelCase__					,     cross_attention_kwargs=lowerCAmelCase__					,     return_dict=lowerCAmelCase__					,     )[0]
																				encoded_states.append(encoded_state - input_states					)
																				tokens_start += self.condition_lengths[i]

													SCREAMING_SNAKE_CASE       :   List[Any]         =      encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
													SCREAMING_SNAKE_CASE       :   int         =      output_states + input_states

													if not return_dict:
																				return (output_states,)

													return TransformeraDModelOutput(sample=lowerCAmelCase__					)









'''simple docstring'''



from math import pow
def        UpperCAmelCase (    A	:					int       ,		A	:					int       ,		A	:					int       ,		A	:					int       ,		A	:					int       ,		):
							if current_sum == needed_sum:
														# If the sum of the powers is equal to needed_sum, then we have a solution.
														solutions_count += 1
														return current_sum, solutions_count

							SCREAMING_SNAKE_CASE       :   Tuple         =      int(pow(A       ,		A					)					)
							if current_sum + i_to_n <= needed_sum:
														# If the sum of the powers is less than needed_sum, then continue adding powers.
														current_sum += i_to_n
														SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   Dict         =      backtrack(
														    A       ,		A       ,		current_number + 1       ,		A       ,		A					)
														current_sum -= i_to_n
							if i_to_n < needed_sum:
														# If the power of i is less than needed_sum, then try with the next power.
														SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   int         =      backtrack(
														    A       ,		A       ,		current_number + 1       ,		A       ,		A					)
							return current_sum, solutions_count
def        UpperCAmelCase (    A	:					int       ,		A	:					int					):
							if not (1 <= needed_sum <= 1000 and 2 <= power <= 10):
														raise ValueError(
														    '''Invalid input\n'''
														    '''needed_sum must be between 1 and 1000, power between 2 and 10.'''					)

							return backtrack(A       ,		A       ,		1       ,		0       ,		0					)[1]  # Return the solutions_count


if __name__ == "__main__":
							import doctest

							doctest.testmod()




